scholarly journals Rain gauge network design for flood forecasting using multi-criteria decision analysis and clustering techniques in lower Mahanadi river basin, India

2015 ◽  
Vol 4 ◽  
pp. 313-332 ◽  
Author(s):  
Anil Kumar Kar ◽  
A.K. Lohani ◽  
N.K. Goel ◽  
G.P. Roy
Keyword(s):  
Decision Analysis ◽  
Network Design ◽  
River Basin ◽  
Flood Forecasting ◽  
Rain Gauge ◽  
Clustering Techniques ◽  
Mahanadi River ◽  
Multi Criteria Decision Analysis ◽  
Rain Gauge Network

Rule-based rain gauge network design in urban areas aided by spatial kernel density

2016 ◽  
Vol 11 (1) ◽  
pp. 166-175 ◽  
Author(s):  
Changfeng Jing ◽  
Jianjun Yu ◽  
Peipei Dai ◽  
Haiyang Wei ◽  
Mingyi Du
Keyword(s):  
Network Design ◽  
Urban Areas ◽  
Residential Buildings ◽  
Scientific Evidence ◽  
Kernel Density ◽  
Rain Gauge ◽  
Signal Interference ◽  
Site Location ◽  
Rule Based ◽  
Rain Gauge Network

An algorithm of rule-based rain gauge network design in urban areas was proposed in this study. We summarized three general criteria to select the sites of rain gauges, including: (i) installment in open space; (ii) priority consideration of important regions and even distribution; and (iii) keep strong signal and avoid weak interference. Aided by spatial kernel density, the candidate locations were determined through clustering the residential buildings at first. Secondly, the overlay and buffer spatial analyses were carried out to optimize the candidate sites to avoid signal interference. Finally, the quality of site location was evaluated by cross-validation in using observed historical rainfall and ranked by mean square error for final consideration. A study case in Xicheng district, Beijing, China was selected to demonstrate the proposed method. The result showed that it could be well applied in urban areas with the capability of considering complex urban features through defining rules. It thus could provide scientific evidence for decision making in rain gauge site selection.

scholarly journals Comparison and Analysis of Different Discrete Methods and Entropy-Based Methods in Rain Gauge Network Design

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1357
Author(s):  
Yanyan Huang ◽  
Hongli Zhao ◽  
Yunzhong Jiang ◽  
Xin Lu ◽  
Zheng Hao ◽  
...  
Keyword(s):  
Network Design ◽  
Rainy Season ◽  
Rain Gauge ◽  
Rain Gauge Data ◽  
Network Information ◽  
Discretization Methods ◽  
Network Layout ◽  
Development And Utilization ◽  
Discrete Methods ◽  
Rain Gauge Network

A reasonable rain gauge network layout can provide accurate regional rainfall data and effectively support the monitoring, development and utilization of water resources. Currently, an increasing number of network design methods based on entropy targets are being applied to network design. The discretization of data is a common method of obtaining the probability in calculations of information entropy. To study the application of different discretization methods and different entropy-based methods in the design of rain gauge networks, this paper compares and analyzes 9 design results for rainy season rain gauge networks using three commonly used discretization methods (A1, SC and ST) and three entropy-based network design algorithms (MIMR, HT and HC) from three perspectives: the joint entropy, spatiality, and accuracy of the network, as evaluation indices. The results show that the variation in network information calculated by the A1 and ST methods for rainy season rain gauge data is too large or too small compared to that calculated by the SC method, and also that the MIMR method performs better in terms of spatiality and accuracy than the HC and HT methods. The comparative analysis results provide a reference for the selection of discrete methods and entropy-based objectives in rain gauge network design, and provides a way to explore a more suitable rain gauge network layout scheme.

scholarly journals Areal rainfall estimation using moving cars as rain gauges – a modelling study

2009 ◽  
Vol 6 (4) ◽  
pp. 4737-4772
Author(s):  
U. Haberlandt ◽  
M. Sester
Keyword(s):  
River Basin ◽  
Research Question ◽  
Rain Gauge ◽  
Indicator Kriging ◽  
High Time Resolution ◽  
Rainfall Estimation ◽  
Time Step ◽  
Rain Gauges ◽  
Areal Rainfall ◽  
Rain Gauge Network

Abstract. Optimal spatial assessment of short-time step precipitation for hydrological modelling is still an important research question considering the poor observation networks for high time resolution data. The main objective of this paper is to present a new approach for rainfall observation. The idea is to consider motorcars as moving rain gauges with windscreen wipers as sensors to detect precipitation. This idea is easily technically feasible if the cars are provided with GPS and a small memory chip for recording the coordinates, car speed and wiper frequency. This study explores theoretically the benefits of such an approach. For that a valid relationship between wiper speed and rainfall rate considering uncertainty was assumed here. A simple traffic model is applied to generate motorcars on roads in a river basin. Radar data are used as reference truth rainfall fields. Rainfall from these fields is sampled with a conventional rain gauge network and with several dynamic networks consisting of moving motorcars. Those observed point rainfall data from the different networks are then used to calculate areal rainfall for different scales. Ordinary kriging and indicator kriging are applied for interpolation of the point data with the latter considering uncertain rainfall observation by cars e.g. according to a discrete number of windscreen wiper operation classes. The results are compared with the true values from the radar observations. The study is carried out for the 3300 km2 Bode river basin located in the Harz Mountains in Northern Germany. The results show, that the idea is theoretically feasible. Only a small portion of the cars needed to be equipped with sensors for sufficient areal rainfall estimation. Regarding the required sensitivity of the potential rain sensors in cars it could be shown, that often a few classes for rainfall observation are enough for satisfactory areal rainfall estimation. The findings of the study suggest also a revisiting of the rain gauge network optimisation problem.

scholarly journals Flood Forecasting in Large River Basins Using FOSS Tool and HPC

Water ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 3484
Author(s):  
Upasana Dutta ◽  
Yogesh Kumar Singh ◽  
T. S. Murugesh Prabhu ◽  
Girishchandra Yendargaye ◽  
Rohini Gopinath Kale ◽  
...  
Keyword(s):  
Open Source ◽  
River Basin ◽  
Indian Subcontinent ◽  
River Basins ◽  
Flood Forecasting ◽  
Economic Losses ◽  
Design Development ◽  
Irreversible Damage ◽  
Flood Prediction ◽  
Mahanadi River

The Indian subcontinent is annually affected by floods that cause profound irreversible damage to crops and livelihoods. With increased incidences of floods and their related catastrophes, the design, development, and deployment of an Early Warning System for Flood Prediction (EWS-FP) for the river basins of India is needed, along with timely dissemination of flood-related information for mitigation of disaster impacts. Accurately drafted and disseminated early warnings/advisories may significantly reduce economic losses incurred due to floods. This study describes the design and development of an EWS-FP using advanced computational tools/methods, viz. HPC, remote sensing, GIS technologies, and open-source tools for the Mahanadi River Basin of India. The flood prediction is based on a robust 2D hydrodynamic model, which solves shallow water equations using the finite volume method. The model is open-source, supports geographic file formats, and is capable of simulating rainfall run-off, river routing, and tidal forcing, simultaneously. The model was tested for a part of the Mahanadi River Basin (Mahanadi Delta, 9225 sq km) with actual and predicted discharge, rainfall, and tide data. The simulated flood inundation spread and stage were compared with SAR data and CWC Observed Gauge data, respectively. The system shows good accuracy and better lead time suitable for flood forecasting in near-real-time.

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